Does Dynamic-Point Filtering Help When Texture Is Scarce? A Controlled Study of ORB-SLAM2 Front-Ends in Synthetic Indoor Scenes
Read the original on arXiv Computer Vision →The study evaluates whether dynamic-point filtering improves ORB‑SLAM2 performance in low‑texture indoor scenes by systematically varying texture and scene dynamics in synthetic sequences. Using stereo, RGB‑D, and ground‑truth data, the authors compare ORB‑SLAM2 without filtering, with an optical‑flow/epipolar filter (FLOW), and with a multi‑view depth‑consistency filter (GEOM), measuring trajectory error, tracking completeness, and static‑feature retention. Results show filtering mainly helps in highly dynamic environments; at the lowest texture level it reduces tracking completeness, and ORB‑SLAM2 fails to initialize in static low‑texture scenes, with GEOM discarding more static keypoints than FLOW. "whyItMatters":"The findings challenge the assumption that dynamic‑point filtering always benefits SLAM, highlighting its limited effectiveness in low‑texture settings and guiding future filter design."
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